AI0-001 Machine Learning and Deep Learning Practice Question
Exhibit
Predicted Negative Predicted Positive Actual Negative 9000 100 Actual Positive 500 400
Refer to the exhibit. What is the recall of the model?
⚠ Common exam trap
CompTIA often tests the confusion between recall and precision, so candidates mistakenly compute precision (TP/(TP+FP)) instead of recall, leading them to choose 0.80.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
0.44
Recall is calculated as True Positives divided by (True Positives + False Negatives). From the confusion matrix in the exhibit, True Positives = 400 and False Negatives = 500, so recall = 400 / (400 + 500) = 400 / 900 ≈ 0.44. Option A is correct because this matches the computed recall value.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
0.44
Why this is correct
Recall equals true positives divided by the sum of true positives and false negatives. Reading the confusion matrix, 0.44 results from that ratio, giving the proportion of actual positives the model correctly identified. This satisfies the stem's request for the recall value derived from the exhibit.
- ✗
0.80
Why it's wrong here
0.80 is the precision (TP/(TP+FP) = 400/500 = 0.8).
- ✗
0.90
Why it's wrong here
0.90 is the specificity (TN/(TN+FP) = 9000/9100 ≈ 0.989, not 0.90).
- ✗
0.99
Why it's wrong here
0.99 is the accuracy ((9000+400)/10000 = 0.94, not 0.99).
About these practice questions
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Written by Johnson Ajibi, MSc IT Security
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This AI0-001 practice question is part of Courseiva's free CompTIA certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AI0-001 exam.